{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Word Vectors with word2vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we create word vectors from a corpus of public-domain books, a selection from [Project Gutenberg](https://www.gutenberg.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize, sent_tokenize\n",
    "import gensim\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.plotting import show, figure\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt') # English-language sentence tokenizer (not all periods end sentences; not all sentences start with a capital letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package gutenberg to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package gutenberg is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('gutenberg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import gutenberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gutenberg.fileids())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenize text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gberg_sent_tokens = sent_tokenize(gutenberg.raw())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[Emma by Jane Austen 1816]\\n\\nVOLUME I\\n\\nCHAPTER I\\n\\n\\nEmma Woodhouse, handsome, clever, and rich, with a comfortable home\\nand happy disposition, seemed to unite some of the best blessings\\nof existence; and had lived nearly twenty-one years in the world\\nwith very little to distress or vex her.',\n",
       " \"She was the youngest of the two daughters of a most affectionate,\\nindulgent father; and had, in consequence of her sister's marriage,\\nbeen mistress of his house from a very early period.\",\n",
       " 'Her mother\\nhad died too long ago for her to have more than an indistinct\\nremembrance of her caresses; and her place had been supplied\\nby an excellent woman as governess, who had fallen little short\\nof a mother in affection.',\n",
       " \"Sixteen years had Miss Taylor been in Mr. Woodhouse's family,\\nless as a governess than a friend, very fond of both daughters,\\nbut particularly of Emma.\",\n",
       " 'Between _them_ it was more the intimacy\\nof sisters.']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sent_tokens[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"She was the youngest of the two daughters of a most affectionate,\\nindulgent father; and had, in consequence of her sister's marriage,\\nbeen mistress of his house from a very early period.\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sent_tokens[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['She',\n",
       " 'was',\n",
       " 'the',\n",
       " 'youngest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'of',\n",
       " 'a',\n",
       " 'most',\n",
       " 'affectionate',\n",
       " ',',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " ';',\n",
       " 'and',\n",
       " 'had',\n",
       " ',',\n",
       " 'in',\n",
       " 'consequence',\n",
       " 'of',\n",
       " 'her',\n",
       " 'sister',\n",
       " \"'s\",\n",
       " 'marriage',\n",
       " ',',\n",
       " 'been',\n",
       " 'mistress',\n",
       " 'of',\n",
       " 'his',\n",
       " 'house',\n",
       " 'from',\n",
       " 'a',\n",
       " 'very',\n",
       " 'early',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(gberg_sent_tokens[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'father'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(gberg_sent_tokens[1])[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# a convenient method that handles newlines, as well as tokenizing sentences and words in one shot\n",
    "gberg_sents = gutenberg.sents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']'],\n",
       " ['VOLUME', 'I'],\n",
       " ['CHAPTER', 'I'],\n",
       " ['Emma',\n",
       "  'Woodhouse',\n",
       "  ',',\n",
       "  'handsome',\n",
       "  ',',\n",
       "  'clever',\n",
       "  ',',\n",
       "  'and',\n",
       "  'rich',\n",
       "  ',',\n",
       "  'with',\n",
       "  'a',\n",
       "  'comfortable',\n",
       "  'home',\n",
       "  'and',\n",
       "  'happy',\n",
       "  'disposition',\n",
       "  ',',\n",
       "  'seemed',\n",
       "  'to',\n",
       "  'unite',\n",
       "  'some',\n",
       "  'of',\n",
       "  'the',\n",
       "  'best',\n",
       "  'blessings',\n",
       "  'of',\n",
       "  'existence',\n",
       "  ';',\n",
       "  'and',\n",
       "  'had',\n",
       "  'lived',\n",
       "  'nearly',\n",
       "  'twenty',\n",
       "  '-',\n",
       "  'one',\n",
       "  'years',\n",
       "  'in',\n",
       "  'the',\n",
       "  'world',\n",
       "  'with',\n",
       "  'very',\n",
       "  'little',\n",
       "  'to',\n",
       "  'distress',\n",
       "  'or',\n",
       "  'vex',\n",
       "  'her',\n",
       "  '.'],\n",
       " ['She',\n",
       "  'was',\n",
       "  'the',\n",
       "  'youngest',\n",
       "  'of',\n",
       "  'the',\n",
       "  'two',\n",
       "  'daughters',\n",
       "  'of',\n",
       "  'a',\n",
       "  'most',\n",
       "  'affectionate',\n",
       "  ',',\n",
       "  'indulgent',\n",
       "  'father',\n",
       "  ';',\n",
       "  'and',\n",
       "  'had',\n",
       "  ',',\n",
       "  'in',\n",
       "  'consequence',\n",
       "  'of',\n",
       "  'her',\n",
       "  'sister',\n",
       "  \"'\",\n",
       "  's',\n",
       "  'marriage',\n",
       "  ',',\n",
       "  'been',\n",
       "  'mistress',\n",
       "  'of',\n",
       "  'his',\n",
       "  'house',\n",
       "  'from',\n",
       "  'a',\n",
       "  'very',\n",
       "  'early',\n",
       "  'period',\n",
       "  '.']]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['She',\n",
       " 'was',\n",
       " 'the',\n",
       " 'youngest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'of',\n",
       " 'a',\n",
       " 'most',\n",
       " 'affectionate',\n",
       " ',',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " ';',\n",
       " 'and',\n",
       " 'had',\n",
       " ',',\n",
       " 'in',\n",
       " 'consequence',\n",
       " 'of',\n",
       " 'her',\n",
       " 'sister',\n",
       " \"'\",\n",
       " 's',\n",
       " 'marriage',\n",
       " ',',\n",
       " 'been',\n",
       " 'mistress',\n",
       " 'of',\n",
       " 'his',\n",
       " 'house',\n",
       " 'from',\n",
       " 'a',\n",
       " 'very',\n",
       " 'early',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'father'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[4][14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', ...]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another convenient method that we don't immediately need: \n",
    "gutenberg.words() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# gutenberg.words() is analogous to the following line, which need not be run: \n",
    "# word_tokenize(gutenberg.raw())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2621613"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our Gutenberg corpus is 2.6m words in length: \n",
    "len(gutenberg.words())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model = Word2Vec(sentences=gberg_sents, size=64, sg=1, window=10, min_count=5, seed=42, workers=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model.save('raw_gutenberg_model.w2v')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# skip re-training the model with the next line:  \n",
    "model = gensim.models.Word2Vec.load('raw_gutenberg_model.w2v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.60053289e-01,  -1.57095745e-01,  -1.35954544e-01,\n",
       "         2.11573735e-01,  -1.07136287e-01,   8.77310261e-02,\n",
       "         4.14559871e-01,   2.26700798e-01,   2.97906458e-01,\n",
       "         5.01078293e-02,   1.78846210e-01,   1.08067788e-01,\n",
       "        -4.35526311e-01,  -4.25833821e-01,  -2.53711820e-01,\n",
       "         7.52079263e-02,  -5.83147258e-02,   3.74293029e-01,\n",
       "        -6.66874468e-01,   2.33097542e-02,   1.35770917e-01,\n",
       "         1.64983049e-01,  -1.20256573e-01,   4.77150202e-01,\n",
       "         1.22489601e-01,   3.75590801e-01,   1.48467004e-01,\n",
       "         2.42270246e-01,   3.16664786e-03,  -7.44531572e-01,\n",
       "        -6.36309106e-03,   4.52868968e-01,   2.24508166e-01,\n",
       "         2.70326473e-02,   1.53943673e-01,   2.01649368e-01,\n",
       "        -1.73006937e-01,  -6.66332617e-02,   1.03040524e-01,\n",
       "        -1.66178823e-01,   2.01654643e-01,   2.97852069e-01,\n",
       "        -8.91307294e-02,  -3.24958622e-01,   8.13202839e-03,\n",
       "         1.84750110e-02,  -1.02419108e-01,   1.29145041e-01,\n",
       "         1.63626075e-01,  -2.15422258e-01,  -2.92296857e-01,\n",
       "        -5.65005839e-01,  -1.61351681e-01,  -2.04182118e-02,\n",
       "        -2.52595842e-01,   4.30920184e-01,  -1.48654059e-01,\n",
       "         4.11218196e-01,   6.62949324e-01,  -5.52654732e-04,\n",
       "        -2.41263226e-01,  -7.45767877e-02,  -4.33727890e-01,\n",
       "         6.22240841e-01], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model['dog']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model['dog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('puppy', 0.8146849870681763),\n",
       " ('sweeper', 0.7880833148956299),\n",
       " ('broth', 0.7820672988891602),\n",
       " ('cage', 0.7675053477287292),\n",
       " ('pig', 0.7646769285202026),\n",
       " ('pet', 0.7610172033309937),\n",
       " ('fox', 0.7553994059562683),\n",
       " ('Truck', 0.7543582320213318),\n",
       " ('Lightfoot', 0.742825984954834),\n",
       " ('cow', 0.7392480373382568)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('dog') # distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('suppose', 0.8497018814086914),\n",
       " ('manage', 0.8408315181732178),\n",
       " ('know', 0.8337839841842651),\n",
       " ('contradict', 0.8264023661613464),\n",
       " ('interfere', 0.8262845277786255),\n",
       " ('NOW', 0.8262041211128235),\n",
       " ('Mamma', 0.8196532726287842),\n",
       " ('believe', 0.8150398135185242),\n",
       " ('shouldn', 0.8124459981918335),\n",
       " ('Williams', 0.8068450689315796)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('think')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('morning', 0.8098249435424805),\n",
       " ('night', 0.7679969072341919),\n",
       " ('month', 0.7410775423049927),\n",
       " ('evening', 0.7385483384132385),\n",
       " ('time', 0.7339625358581543),\n",
       " ('week', 0.7182490825653076),\n",
       " ('feasting', 0.7168370485305786),\n",
       " ('Saturday', 0.7018452286720276),\n",
       " ('Adar', 0.6979250311851501),\n",
       " ('morrow', 0.6971378922462463)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mother', 0.8728880882263184),\n",
       " ('brother', 0.8333652019500732),\n",
       " ('sister', 0.7919358015060425),\n",
       " ('wife', 0.7858221530914307),\n",
       " ('daughter', 0.7800713181495667),\n",
       " ('Amnon', 0.7543610334396362),\n",
       " ('Tamar', 0.7502950429916382),\n",
       " ('servant', 0.7387294769287109),\n",
       " ('uncle', 0.7327381372451782),\n",
       " ('curseth', 0.7289719581604004)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('father')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dog'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.doesnt_match(\"mother father daughter dog\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49294278479463099"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.similarity('father', 'dog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('daughter', 0.7782288193702698),\n",
       " ('sister', 0.777533769607544),\n",
       " ('mother', 0.7643921971321106),\n",
       " ('wife', 0.7588695287704468),\n",
       " ('husband', 0.7556524276733398),\n",
       " ('Tamar', 0.7055943012237549),\n",
       " ('Rachel', 0.7008661031723022),\n",
       " ('conceived', 0.6945146322250366),\n",
       " ('Sarah', 0.6943398714065552),\n",
       " ('brother', 0.6798239946365356)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# close, but not quite; distinctly in female direction: \n",
    "model.most_similar(positive=['father', 'woman'], negative=['man']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('daughter', 0.7534747123718262),\n",
       " ('Leah', 0.724845290184021),\n",
       " ('Hagar', 0.724432110786438),\n",
       " ('Rachel', 0.7239198684692383),\n",
       " ('conceived', 0.7235168218612671),\n",
       " ('Sarah', 0.7165275812149048),\n",
       " ('wife', 0.713229775428772),\n",
       " ('Rebekah', 0.6989840269088745),\n",
       " ('Caleb', 0.6943678259849548),\n",
       " ('Sarai', 0.6914916038513184)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more confident about this one: \n",
    "model.most_similar(positive=['son', 'woman'], negative=['man']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wife', 0.708131730556488),\n",
       " ('sister', 0.6937975287437439),\n",
       " ('daughter', 0.68210768699646),\n",
       " ('child', 0.66499924659729),\n",
       " ('maid', 0.663989782333374),\n",
       " ('conceived', 0.6550735235214233),\n",
       " ('mother', 0.6408793926239014),\n",
       " ('nurse', 0.6267993450164795),\n",
       " ('elder', 0.6201719045639038),\n",
       " ('widow', 0.6188254952430725)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(positive=['husband', 'woman'], negative=['man']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Rachel', 0.7381176352500916),\n",
       " ('Sarah', 0.7235167026519775),\n",
       " ('Laban', 0.7040327191352844),\n",
       " ('Leah', 0.703708291053772),\n",
       " ('Padanaram', 0.7029510736465454),\n",
       " ('Hagar', 0.6949448585510254),\n",
       " ('Rebekah', 0.6927774548530579),\n",
       " ('Pharaoh', 0.6861628293991089),\n",
       " ('Abram', 0.6841262578964233),\n",
       " ('Hamor', 0.6762512922286987),\n",
       " ('Shechem', 0.6723358631134033),\n",
       " ('daughter', 0.6721547842025757),\n",
       " ('damsel', 0.6675446033477783),\n",
       " ('Abimelech', 0.6638238430023193),\n",
       " ('Ephron', 0.6627167463302612),\n",
       " ('Bethuel', 0.6601616144180298),\n",
       " ('Esau', 0.6585861444473267),\n",
       " ('Solomon', 0.6583372354507446),\n",
       " ('Bilhah', 0.6578104496002197),\n",
       " ('household', 0.6573641300201416),\n",
       " ('Jerubbaal', 0.655084490776062),\n",
       " ('conceived', 0.6541304588317871),\n",
       " ('Judah', 0.652094304561615),\n",
       " ('Sarai', 0.649071991443634),\n",
       " ('Lot', 0.6466783881187439),\n",
       " ('Zilpah', 0.6465750932693481),\n",
       " ('birthright', 0.6449142694473267),\n",
       " ('queen', 0.644464910030365),\n",
       " ('David', 0.6373385190963745),\n",
       " ('Rahab', 0.6370617151260376)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(positive=['king', 'woman'], negative=['man'], topn=30) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# impressive for such a small data set, without any cleaning, e.g., to lower case (covered next)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reduce word vector dimensionality with t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'[': <gensim.models.keyedvectors.Vocab at 0x7fbfce6410f0>,\n",
       " 'Emma': <gensim.models.keyedvectors.Vocab at 0x7fbfce655da0>,\n",
       " 'by': <gensim.models.keyedvectors.Vocab at 0x7fbfce655b70>,\n",
       " 'Jane': <gensim.models.keyedvectors.Vocab at 0x7fbfce655a20>,\n",
       " ']': <gensim.models.keyedvectors.Vocab at 0x7fbfce655518>,\n",
       " 'I': <gensim.models.keyedvectors.Vocab at 0x7fbfce655550>,\n",
       " 'CHAPTER': <gensim.models.keyedvectors.Vocab at 0x7fbfce6558d0>,\n",
       " 'Woodhouse': <gensim.models.keyedvectors.Vocab at 0x7fbfce655f28>,\n",
       " ',': <gensim.models.keyedvectors.Vocab at 0x7fbfce655f98>,\n",
       " 'handsome': <gensim.models.keyedvectors.Vocab at 0x7fbfce655dd8>,\n",
       " 'clever': <gensim.models.keyedvectors.Vocab at 0x7fbfce655e80>,\n",
       " 'and': <gensim.models.keyedvectors.Vocab at 0x7fbfce655978>,\n",
       " 'rich': <gensim.models.keyedvectors.Vocab at 0x7fbfce655ef0>,\n",
       " 'with': <gensim.models.keyedvectors.Vocab at 0x7fbfce6559b0>,\n",
       " 'a': <gensim.models.keyedvectors.Vocab at 0x7fbfce655c88>,\n",
       " 'comfortable': <gensim.models.keyedvectors.Vocab at 0x7fbfce655828>,\n",
       " 'home': <gensim.models.keyedvectors.Vocab at 0x7fbfce655d68>,\n",
       " 'happy': <gensim.models.keyedvectors.Vocab at 0x7fbfce655588>,\n",
       " 'disposition': <gensim.models.keyedvectors.Vocab at 0x7fbfce6555c0>,\n",
       " 'seemed': <gensim.models.keyedvectors.Vocab at 0x7fbfce655630>,\n",
       " 'to': <gensim.models.keyedvectors.Vocab at 0x7fbfce6556a0>,\n",
       " 'unite': <gensim.models.keyedvectors.Vocab at 0x7fbfce655710>,\n",
       " 'some': <gensim.models.keyedvectors.Vocab at 0x7fbfce657048>,\n",
       " 'of': <gensim.models.keyedvectors.Vocab at 0x7fbfce6570b8>,\n",
       " 'the': <gensim.models.keyedvectors.Vocab at 0x7fbfce657128>,\n",
       " 'best': <gensim.models.keyedvectors.Vocab at 0x7fbfce657198>,\n",
       " 'blessings': <gensim.models.keyedvectors.Vocab at 0x7fbfce6571d0>,\n",
       " 'existence': <gensim.models.keyedvectors.Vocab at 0x7fbfce657208>,\n",
       " ';': <gensim.models.keyedvectors.Vocab at 0x7fbfce657240>,\n",
       " 'had': <gensim.models.keyedvectors.Vocab at 0x7fbfce6572b0>,\n",
       " 'lived': <gensim.models.keyedvectors.Vocab at 0x7fbfce657320>,\n",
       " 'nearly': <gensim.models.keyedvectors.Vocab at 0x7fbfce657390>,\n",
       " 'twenty': <gensim.models.keyedvectors.Vocab at 0x7fbfce657400>,\n",
       " '-': <gensim.models.keyedvectors.Vocab at 0x7fbfce657438>,\n",
       " 'one': <gensim.models.keyedvectors.Vocab at 0x7fbfce6574a8>,\n",
       " 'years': <gensim.models.keyedvectors.Vocab at 0x7fbfce657518>,\n",
       " 'in': <gensim.models.keyedvectors.Vocab at 0x7fbfce657588>,\n",
       " 'world': <gensim.models.keyedvectors.Vocab at 0x7fbfce6575f8>,\n",
       " 'very': <gensim.models.keyedvectors.Vocab at 0x7fbfce657668>,\n",
       " 'little': <gensim.models.keyedvectors.Vocab at 0x7fbfce6576d8>,\n",
       " 'distress': <gensim.models.keyedvectors.Vocab at 0x7fbfce657710>,\n",
       " 'or': <gensim.models.keyedvectors.Vocab at 0x7fbfce657780>,\n",
       " 'vex': <gensim.models.keyedvectors.Vocab at 0x7fbfce6577f0>,\n",
       " 'her': <gensim.models.keyedvectors.Vocab at 0x7fbfce657860>,\n",
       " '.': <gensim.models.keyedvectors.Vocab at 0x7fbfce657898>,\n",
       " 'She': <gensim.models.keyedvectors.Vocab at 0x7fbfce657908>,\n",
       " 'was': <gensim.models.keyedvectors.Vocab at 0x7fbfce657978>,\n",
       " 'youngest': <gensim.models.keyedvectors.Vocab at 0x7fbfce6579b0>,\n",
       " 'two': <gensim.models.keyedvectors.Vocab at 0x7fbfce657a20>,\n",
       " 'daughters': <gensim.models.keyedvectors.Vocab at 0x7fbfce657a58>,\n",
       " 'most': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ac8>,\n",
       " 'affectionate': <gensim.models.keyedvectors.Vocab at 0x7fbfce657b00>,\n",
       " 'indulgent': <gensim.models.keyedvectors.Vocab at 0x7fbfce657b38>,\n",
       " 'father': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ba8>,\n",
       " 'consequence': <gensim.models.keyedvectors.Vocab at 0x7fbfce657be0>,\n",
       " 'sister': <gensim.models.keyedvectors.Vocab at 0x7fbfce657c50>,\n",
       " \"'\": <gensim.models.keyedvectors.Vocab at 0x7fbfce657c88>,\n",
       " 's': <gensim.models.keyedvectors.Vocab at 0x7fbfce657cc0>,\n",
       " 'marriage': <gensim.models.keyedvectors.Vocab at 0x7fbfce657cf8>,\n",
       " 'been': <gensim.models.keyedvectors.Vocab at 0x7fbfce657d68>,\n",
       " 'mistress': <gensim.models.keyedvectors.Vocab at 0x7fbfce657da0>,\n",
       " 'his': <gensim.models.keyedvectors.Vocab at 0x7fbfce657e10>,\n",
       " 'house': <gensim.models.keyedvectors.Vocab at 0x7fbfce657e80>,\n",
       " 'from': <gensim.models.keyedvectors.Vocab at 0x7fbfce657ef0>,\n",
       " 'early': <gensim.models.keyedvectors.Vocab at 0x7fbfce657f60>,\n",
       " 'period': <gensim.models.keyedvectors.Vocab at 0x7fbfce657fd0>,\n",
       " 'Her': <gensim.models.keyedvectors.Vocab at 0x7fbfce663080>,\n",
       " 'mother': <gensim.models.keyedvectors.Vocab at 0x7fbfce6630f0>,\n",
       " 'died': <gensim.models.keyedvectors.Vocab at 0x7fbfce663160>,\n",
       " 'too': <gensim.models.keyedvectors.Vocab at 0x7fbfce6631d0>,\n",
       " 'long': <gensim.models.keyedvectors.Vocab at 0x7fbfce663240>,\n",
       " 'ago': <gensim.models.keyedvectors.Vocab at 0x7fbfce6632b0>,\n",
       " 'for': <gensim.models.keyedvectors.Vocab at 0x7fbfce663320>,\n",
       " 'have': <gensim.models.keyedvectors.Vocab at 0x7fbfce663390>,\n",
       " 'more': <gensim.models.keyedvectors.Vocab at 0x7fbfce663400>,\n",
       " 'than': <gensim.models.keyedvectors.Vocab at 0x7fbfce663470>,\n",
       " 'an': <gensim.models.keyedvectors.Vocab at 0x7fbfce6634e0>,\n",
       " 'remembrance': <gensim.models.keyedvectors.Vocab at 0x7fbfce663518>,\n",
       " 'caresses': <gensim.models.keyedvectors.Vocab at 0x7fbfce663550>,\n",
       " 'place': <gensim.models.keyedvectors.Vocab at 0x7fbfce6635c0>,\n",
       " 'supplied': <gensim.models.keyedvectors.Vocab at 0x7fbfce6635f8>,\n",
       " 'excellent': <gensim.models.keyedvectors.Vocab at 0x7fbfce663630>,\n",
       " 'woman': <gensim.models.keyedvectors.Vocab at 0x7fbfce6636a0>,\n",
       " 'as': <gensim.models.keyedvectors.Vocab at 0x7fbfce663710>,\n",
       " 'governess': <gensim.models.keyedvectors.Vocab at 0x7fbfce663748>,\n",
       " 'who': <gensim.models.keyedvectors.Vocab at 0x7fbfce6637b8>,\n",
       " 'fallen': <gensim.models.keyedvectors.Vocab at 0x7fbfce663828>,\n",
       " 'short': <gensim.models.keyedvectors.Vocab at 0x7fbfce663898>,\n",
       " 'affection': <gensim.models.keyedvectors.Vocab at 0x7fbfce6638d0>,\n",
       " 'Sixteen': <gensim.models.keyedvectors.Vocab at 0x7fbfce663940>,\n",
       " 'Miss': <gensim.models.keyedvectors.Vocab at 0x7fbfce6639b0>,\n",
       " 'Taylor': <gensim.models.keyedvectors.Vocab at 0x7fbfce663a20>,\n",
       " 'Mr': <gensim.models.keyedvectors.Vocab at 0x7fbfce663a90>,\n",
       " 'family': <gensim.models.keyedvectors.Vocab at 0x7fbfce663b00>,\n",
       " 'less': <gensim.models.keyedvectors.Vocab at 0x7fbfce663b70>,\n",
       " 'friend': <gensim.models.keyedvectors.Vocab at 0x7fbfce663be0>,\n",
       " 'fond': <gensim.models.keyedvectors.Vocab at 0x7fbfce663c50>,\n",
       " 'both': <gensim.models.keyedvectors.Vocab at 0x7fbfce663cc0>,\n",
       " 'but': <gensim.models.keyedvectors.Vocab at 0x7fbfce663d30>,\n",
       " 'particularly': <gensim.models.keyedvectors.Vocab at 0x7fbfce663d68>,\n",
       " 'Between': <gensim.models.keyedvectors.Vocab at 0x7fbfce663dd8>,\n",
       " 'it': <gensim.models.keyedvectors.Vocab at 0x7fbfce663e48>,\n",
       " 'intimacy': <gensim.models.keyedvectors.Vocab at 0x7fbfce663e80>,\n",
       " 'sisters': <gensim.models.keyedvectors.Vocab at 0x7fbfce663ef0>,\n",
       " 'Even': <gensim.models.keyedvectors.Vocab at 0x7fbfce663f60>,\n",
       " 'before': <gensim.models.keyedvectors.Vocab at 0x7fbfce663fd0>,\n",
       " 'ceased': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703080>,\n",
       " 'hold': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7030f0>,\n",
       " 'office': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703160>,\n",
       " 'mildness': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703198>,\n",
       " 'temper': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703208>,\n",
       " 'hardly': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703278>,\n",
       " 'allowed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7032e8>,\n",
       " 'impose': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703358>,\n",
       " 'any': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7033c8>,\n",
       " 'restraint': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703400>,\n",
       " 'shadow': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703470>,\n",
       " 'authority': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7034a8>,\n",
       " 'being': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703518>,\n",
       " 'now': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703588>,\n",
       " 'passed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7035f8>,\n",
       " 'away': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703668>,\n",
       " 'they': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7036d8>,\n",
       " 'living': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703748>,\n",
       " 'together': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703780>,\n",
       " 'mutually': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7037b8>,\n",
       " 'attached': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7037f0>,\n",
       " 'doing': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703860>,\n",
       " 'just': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7038d0>,\n",
       " 'what': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703940>,\n",
       " 'she': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7039b0>,\n",
       " 'liked': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703a20>,\n",
       " 'highly': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703a90>,\n",
       " 'judgment': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703ac8>,\n",
       " 'directed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703b00>,\n",
       " 'chiefly': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703b70>,\n",
       " 'own': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703be0>,\n",
       " 'The': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703c50>,\n",
       " 'real': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703cc0>,\n",
       " 'evils': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703d30>,\n",
       " 'indeed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703da0>,\n",
       " 'situation': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703dd8>,\n",
       " 'were': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703e48>,\n",
       " 'power': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703eb8>,\n",
       " 'having': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703f28>,\n",
       " 'rather': <gensim.models.keyedvectors.Vocab at 0x7fbfcd703f98>,\n",
       " 'much': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706048>,\n",
       " 'way': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7060b8>,\n",
       " 'think': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706128>,\n",
       " 'well': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706198>,\n",
       " 'herself': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706208>,\n",
       " 'these': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706278>,\n",
       " 'which': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7062e8>,\n",
       " 'threatened': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706320>,\n",
       " 'alloy': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706390>,\n",
       " 'many': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706400>,\n",
       " 'enjoyments': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706438>,\n",
       " 'danger': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7064a8>,\n",
       " 'however': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706518>,\n",
       " 'at': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706588>,\n",
       " 'present': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7065f8>,\n",
       " 'so': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706668>,\n",
       " 'unperceived': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7066a0>,\n",
       " 'that': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706710>,\n",
       " 'did': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706780>,\n",
       " 'not': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7067f0>,\n",
       " 'means': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706860>,\n",
       " 'rank': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7068d0>,\n",
       " 'misfortunes': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706908>,\n",
       " 'Sorrow': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706978>,\n",
       " 'came': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7069e8>,\n",
       " '--': <gensim.models.keyedvectors.Vocab at 0x7fbfcd706a58>,\n",
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       " 'people': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70c4a8>,\n",
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       " 'left': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70c588>,\n",
       " 'dine': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70c5f8>,\n",
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       " 'third': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70c710>,\n",
       " 'cheer': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70c780>,\n",
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       " 'event': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70cc88>,\n",
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       " 'unexceptionable': <gensim.models.keyedvectors.Vocab at 0x7fbfcd70ceb8>,\n",
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       " 'manners': <gensim.models.keyedvectors.Vocab at 0x7fbfcd713160>,\n",
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       " 'considering': <gensim.models.keyedvectors.Vocab at 0x7fbfcd713240>,\n",
       " 'self': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7132b0>,\n",
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       " 'generous': <gensim.models.keyedvectors.Vocab at 0x7fbfcd713358>,\n",
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       " 'would': <gensim.models.keyedvectors.Vocab at 0x7fbfcd713748>,\n",
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       " 'amuse': <gensim.models.keyedvectors.Vocab at 0x7fbfcd713dd8>,\n",
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       " 'debt': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a128>,\n",
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       " 'here': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a240>,\n",
       " 'intercourse': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a278>,\n",
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       " 'equal': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a3c8>,\n",
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       " 'unreserve': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a4e0>,\n",
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       " 'followed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a588>,\n",
       " 'Isabella': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a5c0>,\n",
       " 'their': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a630>,\n",
       " 'each': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a6a0>,\n",
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       " 'recollection': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a828>,\n",
       " 'companion': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a860>,\n",
       " 'such': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a8d0>,\n",
       " 'few': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a940>,\n",
       " 'possessed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71a978>,\n",
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       " 'informed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71aa20>,\n",
       " 'useful': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71aa90>,\n",
       " 'knowing': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ab00>,\n",
       " 'ways': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ab70>,\n",
       " 'interested': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71aba8>,\n",
       " 'its': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ac18>,\n",
       " 'concerns': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ac50>,\n",
       " 'peculiarly': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ac88>,\n",
       " 'pleasure': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71acc0>,\n",
       " 'scheme': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71ad30>,\n",
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       " 'arose': <gensim.models.keyedvectors.Vocab at 0x7fbfcd71af60>,\n",
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       " 'dearly': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724940>,\n",
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       " 'He': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724a90>,\n",
       " 'meet': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724b00>,\n",
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       " 'playful': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724be0>,\n",
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       " 'disparity': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724cf8>,\n",
       " 'ages': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724d68>,\n",
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       " 'constitution': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724e48>,\n",
       " 'habits': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724eb8>,\n",
       " 'life': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724f28>,\n",
       " 'without': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724f98>,\n",
       " 'activity': <gensim.models.keyedvectors.Vocab at 0x7fbfcd724fd0>,\n",
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       " 'body': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7230f0>,\n",
       " 'older': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723160>,\n",
       " 'though': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7231d0>,\n",
       " 'everywhere': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723208>,\n",
       " 'friendliness': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723240>,\n",
       " 'heart': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7232b0>,\n",
       " 'amiable': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723320>,\n",
       " 'talents': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723390>,\n",
       " 'recommended': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7233c8>,\n",
       " 'him': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723438>,\n",
       " 'time': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7234a8>,\n",
       " 'comparatively': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7234e0>,\n",
       " 'removed': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723550>,\n",
       " 'matrimony': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723588>,\n",
       " 'settled': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7235f8>,\n",
       " 'London': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723668>,\n",
       " 'miles': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7236d8>,\n",
       " 'off': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723748>,\n",
       " 'beyond': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7237b8>,\n",
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       " 'reach': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723898>,\n",
       " 'October': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723908>,\n",
       " 'November': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723940>,\n",
       " 'struggled': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723978>,\n",
       " 'Hartfield': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7239b0>,\n",
       " 'Christmas': <gensim.models.keyedvectors.Vocab at 0x7fbfcd7239e8>,\n",
       " 'next': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723a58>,\n",
       " 'visit': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723ac8>,\n",
       " 'husband': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723b38>,\n",
       " 'children': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723b70>,\n",
       " 'fill': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723be0>,\n",
       " 'give': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723c50>,\n",
       " 'society': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723cc0>,\n",
       " 'again': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723d30>,\n",
       " 'Highbury': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723d68>,\n",
       " 'populous': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723da0>,\n",
       " 'village': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723e10>,\n",
       " 'almost': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723e80>,\n",
       " 'town': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723ef0>,\n",
       " 'spite': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723f60>,\n",
       " 'separate': <gensim.models.keyedvectors.Vocab at 0x7fbfcd723f98>,\n",
       " 'lawn': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d048>,\n",
       " 'shrubberies': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d080>,\n",
       " 'name': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d0f0>,\n",
       " 'really': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d160>,\n",
       " 'belong': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d1d0>,\n",
       " 'afforded': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d208>,\n",
       " 'equals': <gensim.models.keyedvectors.Vocab at 0x7fbfcd72d278>,\n",
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       " 'moonlight': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11b38>,\n",
       " 'mild': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11ba8>,\n",
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       " 'back': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11c88>,\n",
       " 'fire': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11cf8>,\n",
       " 'found': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11d68>,\n",
       " 'damp': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11dd8>,\n",
       " 'dirty': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11e48>,\n",
       " 'catch': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11eb8>,\n",
       " 'cold': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11f28>,\n",
       " 'Look': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd11f98>,\n",
       " 'shoes': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16048>,\n",
       " 'Well': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd160b8>,\n",
       " 'quite': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16128>,\n",
       " 'surprising': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16160>,\n",
       " 'vast': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd161d0>,\n",
       " 'rain': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16240>,\n",
       " 'rained': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd162b0>,\n",
       " 'dreadfully': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd162e8>,\n",
       " 'hard': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16358>,\n",
       " 'breakfast': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16390>,\n",
       " 'wanted': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16400>,\n",
       " 'By': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16470>,\n",
       " 'bye': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd164e0>,\n",
       " 'joy': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16550>,\n",
       " 'Being': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd165c0>,\n",
       " 'sort': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16630>,\n",
       " 'feeling': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd166a0>,\n",
       " 'hurry': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16710>,\n",
       " 'congratulations': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16748>,\n",
       " 'hope': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd167b8>,\n",
       " 'went': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16828>,\n",
       " 'behave': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16898>,\n",
       " 'Who': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16908>,\n",
       " 'cried': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16978>,\n",
       " 'Ah': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd169e8>,\n",
       " 'Tis': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16a58>,\n",
       " 'business': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16a90>,\n",
       " 'please': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16b00>,\n",
       " 'possibly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16b38>,\n",
       " '`': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16b70>,\n",
       " \".'\": <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16be0>,\n",
       " 'regard': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16c50>,\n",
       " 'comes': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16cc0>,\n",
       " 'question': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16cf8>,\n",
       " 'dependence': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16d30>,\n",
       " 'independence': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16d68>,\n",
       " 'At': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16dd8>,\n",
       " 'rate': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16e48>,\n",
       " 'better': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16eb8>,\n",
       " '_one_': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16f28>,\n",
       " 'those': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16f98>,\n",
       " 'fanciful': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd16fd0>,\n",
       " 'troublesome': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e048>,\n",
       " 'creature': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e080>,\n",
       " 'playfully': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e0b8>,\n",
       " 'head': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e128>,\n",
       " 'certainly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e160>,\n",
       " 'believe': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e1d0>,\n",
       " ',\"': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e240>,\n",
       " 'sometimes': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e278>,\n",
       " 'dearest': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e2e8>,\n",
       " 'mean': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e358>,\n",
       " '_you_': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e3c8>,\n",
       " 'horrible': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e400>,\n",
       " 'idea': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e470>,\n",
       " 'Oh': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e4e0>,\n",
       " 'meant': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e550>,\n",
       " 'myself': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e5c0>,\n",
       " 'loves': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e630>,\n",
       " 'joke': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e6a0>,\n",
       " 'another': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e710>,\n",
       " 'fact': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e780>,\n",
       " 'faults': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e7f0>,\n",
       " 'told': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e860>,\n",
       " 'agreeable': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e898>,\n",
       " 'knew': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e908>,\n",
       " 'suspect': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e978>,\n",
       " 'knows': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1e9e8>,\n",
       " 'flatter': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ea58>,\n",
       " 'reflection': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ea90>,\n",
       " 'has': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1eb00>,\n",
       " 'persons': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1eb70>,\n",
       " 'chances': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ebe0>,\n",
       " 'willing': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ec50>,\n",
       " 'let': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ecc0>,\n",
       " 'pass': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ed30>,\n",
       " '--\"': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1eda0>,\n",
       " 'hear': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ee10>,\n",
       " 'behaved': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ee80>,\n",
       " 'charmingly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1eeb8>,\n",
       " 'Every': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ef28>,\n",
       " 'punctual': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1ef60>,\n",
       " 'looks': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd1efd0>,\n",
       " 'tear': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24080>,\n",
       " 'face': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd240f0>,\n",
       " 'seen': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24160>,\n",
       " 'apart': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd241d0>,\n",
       " 'Dear': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24240>,\n",
       " 'bears': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd242b0>,\n",
       " 'sorry': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24320>,\n",
       " 'lose': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24390>,\n",
       " 'miss': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24400>,\n",
       " 'thinks': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24470>,\n",
       " 'turned': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd244e0>,\n",
       " 'divided': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24550>,\n",
       " 'tears': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd245c0>,\n",
       " 'smiles': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24630>,\n",
       " 'should': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd246a0>,\n",
       " 'acceptable': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd246d8>,\n",
       " 'important': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24710>,\n",
       " 'secure': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24780>,\n",
       " 'provision': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd247b8>,\n",
       " 'therefore': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd247f0>,\n",
       " 'allow': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24860>,\n",
       " 'pain': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd248d0>,\n",
       " 'happily': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24940>,\n",
       " 'forgotten': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24978>,\n",
       " 'matter': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd249e8>,\n",
       " 'considerable': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24a20>,\n",
       " 'four': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24a90>,\n",
       " 'proved': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24b00>,\n",
       " 'marry': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24b70>,\n",
       " 'shook': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24be0>,\n",
       " 'fondly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24c50>,\n",
       " 'replied': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24cc0>,\n",
       " 'matches': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24d30>,\n",
       " 'foretell': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24d68>,\n",
       " 'whatever': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24da0>,\n",
       " 'Pray': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24e10>,\n",
       " 'none': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24e80>,\n",
       " 'greatest': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24eb8>,\n",
       " 'amusement': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24ef0>,\n",
       " 'success': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24f60>,\n",
       " 'widower': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd24fd0>,\n",
       " 'perfectly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a048>,\n",
       " 'constantly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a080>,\n",
       " 'occupied': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a0b8>,\n",
       " 'either': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a128>,\n",
       " 'friends': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a198>,\n",
       " 'wherever': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a1d0>,\n",
       " 'need': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a240>,\n",
       " 'spend': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a2b0>,\n",
       " 'single': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a320>,\n",
       " 'year': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a390>,\n",
       " 'alone': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a400>,\n",
       " 'Some': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a470>,\n",
       " 'others': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a4e0>,\n",
       " 'son': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a550>,\n",
       " 'uncle': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a5c0>,\n",
       " 'letting': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a630>,\n",
       " 'solemn': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a6a0>,\n",
       " 'nonsense': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a6d8>,\n",
       " 'subject': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a748>,\n",
       " 'believed': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a780>,\n",
       " 'Ever': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a7f0>,\n",
       " 'since': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a860>,\n",
       " 'met': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a8d0>,\n",
       " 'Broadway': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a908>,\n",
       " 'Lane': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a978>,\n",
       " 'began': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2a9e8>,\n",
       " 'darted': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aa58>,\n",
       " 'gallantry': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aa90>,\n",
       " 'borrowed': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aac8>,\n",
       " 'Farmer': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ab38>,\n",
       " 'planned': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aba8>,\n",
       " 'blessed': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ac18>,\n",
       " 'instance': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ac50>,\n",
       " 'leave': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2acc0>,\n",
       " 'making': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ad30>,\n",
       " 'understand': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ad68>,\n",
       " ',\\'\"': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2add8>,\n",
       " 'supposes': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ae10>,\n",
       " 'endeavour': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ae48>,\n",
       " 'Your': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aeb8>,\n",
       " 'properly': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2aef0>,\n",
       " 'delicately': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2af28>,\n",
       " 'endeavouring': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2af60>,\n",
       " 'bring': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2afd0>,\n",
       " 'worthy': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c080>,\n",
       " 'employment': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c0b8>,\n",
       " 'young': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c128>,\n",
       " 'lady': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c198>,\n",
       " 'imagine': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c208>,\n",
       " 'planning': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c240>,\n",
       " 'saying': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c2b0>,\n",
       " 'yourself': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c2e8>,\n",
       " 'idle': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c358>,\n",
       " \",'\": <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c3c8>,\n",
       " 'why': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c438>,\n",
       " 'talk': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c4a8>,\n",
       " 'Where': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c518>,\n",
       " 'merit': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c588>,\n",
       " 'proud': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c5f8>,\n",
       " 'guess': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c668>,\n",
       " '_that_': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c6d8>,\n",
       " 'can': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c748>,\n",
       " 'known': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c7b8>,\n",
       " 'triumph': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c828>,\n",
       " 'cleverer': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c860>,\n",
       " 'depend': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c8d0>,\n",
       " 'merely': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c940>,\n",
       " 'luck': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2c9b0>,\n",
       " 'There': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ca20>,\n",
       " 'talent': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ca90>,\n",
       " 'word': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cb00>,\n",
       " 'quarrel': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cb70>,\n",
       " 'claim': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cbe0>,\n",
       " 'drawn': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cc50>,\n",
       " 'pictures': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cc88>,\n",
       " 'something': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ccc0>,\n",
       " 'nothing': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cd30>,\n",
       " 'If': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cda0>,\n",
       " 'visits': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ce10>,\n",
       " 'given': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ce80>,\n",
       " 'smoothed': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2ceb8>,\n",
       " 'matters': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cf28>,\n",
       " 'enough': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cf98>,\n",
       " 'comprehend': <gensim.models.keyedvectors.Vocab at 0x7fbfcfd2cfd0>,\n",
       " 'open': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca400>,\n",
       " 'hearted': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cac50>,\n",
       " 'unaffected': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cae10>,\n",
       " 'safely': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cab70>,\n",
       " 'manage': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca940>,\n",
       " 'likely': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca550>,\n",
       " 'harm': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caf28>,\n",
       " 'interference': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caac8>,\n",
       " 'rejoined': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cae48>,\n",
       " 'understanding': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca828>,\n",
       " 'pray': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca1d0>,\n",
       " 'silly': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca390>,\n",
       " 'break': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca5c0>,\n",
       " 'circle': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca2e8>,\n",
       " 'grievously': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca320>,\n",
       " 'Only': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca978>,\n",
       " 'Elton': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca6d8>,\n",
       " ',--': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca240>,\n",
       " 'look': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca080>,\n",
       " 'whole': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca780>,\n",
       " 'fitted': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caa58>,\n",
       " 'comfortably': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca7f0>,\n",
       " 'shame': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca668>,\n",
       " 'longer': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caa20>,\n",
       " 'joining': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca4e0>,\n",
       " 'hands': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cad68>,\n",
       " 'same': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca8d0>,\n",
       " 'service': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca4a8>,\n",
       " 'shew': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cab38>,\n",
       " 'attention': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca630>,\n",
       " 'ask': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caeb8>,\n",
       " 'dare': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cad30>,\n",
       " 'With': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cada0>,\n",
       " 'laughing': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca3c8>,\n",
       " 'fish': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cac18>,\n",
       " 'chicken': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca208>,\n",
       " 'chuse': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ca748>,\n",
       " 'Depend': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cafd0>,\n",
       " 'six': <gensim.models.keyedvectors.Vocab at 0x7fbfce9cab00>,\n",
       " 'care': <gensim.models.keyedvectors.Vocab at 0x7fbfce9caba8>,\n",
       " 'II': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc080>,\n",
       " 'native': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc0f0>,\n",
       " 'born': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc160>,\n",
       " 'respectable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc198>,\n",
       " 'generations': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc1d0>,\n",
       " 'rising': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc240>,\n",
       " 'gentility': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc278>,\n",
       " 'property': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc2b0>,\n",
       " 'received': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc2e8>,\n",
       " 'education': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc320>,\n",
       " 'succeeding': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc358>,\n",
       " 'small': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc3c8>,\n",
       " 'become': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc438>,\n",
       " 'indisposed': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc470>,\n",
       " 'homely': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc4e0>,\n",
       " 'pursuits': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc518>,\n",
       " 'brothers': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc550>,\n",
       " 'engaged': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc5c0>,\n",
       " 'satisfied': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc5f8>,\n",
       " 'active': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc668>,\n",
       " 'social': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc6d8>,\n",
       " 'entering': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc710>,\n",
       " 'militia': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc780>,\n",
       " 'county': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc7f0>,\n",
       " 'embodied': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc828>,\n",
       " 'Captain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc898>,\n",
       " 'general': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc908>,\n",
       " 'favourite': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc940>,\n",
       " 'military': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc978>,\n",
       " 'introduced': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc9b0>,\n",
       " 'Churchill': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dc9e8>,\n",
       " 'Yorkshire': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dca20>,\n",
       " 'fell': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dca90>,\n",
       " 'love': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcb00>,\n",
       " 'surprized': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcb38>,\n",
       " 'except': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcba8>,\n",
       " 'full': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcc18>,\n",
       " 'pride': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcc88>,\n",
       " 'importance': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dccc0>,\n",
       " 'connexion': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dccf8>,\n",
       " 'offend': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcd68>,\n",
       " 'command': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcdd8>,\n",
       " 'bore': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dce48>,\n",
       " 'proportion': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dce80>,\n",
       " 'estate': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcef0>,\n",
       " 'took': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcf60>,\n",
       " 'infinite': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcf98>,\n",
       " 'mortification': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dcfd0>,\n",
       " 'threw': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de080>,\n",
       " 'due': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de0f0>,\n",
       " 'decorum': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de160>,\n",
       " 'unsuitable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de198>,\n",
       " 'produce': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de208>,\n",
       " 'ought': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de278>,\n",
       " 'whose': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de2e8>,\n",
       " 'warm': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de358>,\n",
       " 'sweet': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de3c8>,\n",
       " 'return': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de438>,\n",
       " 'goodness': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de470>,\n",
       " 'spirit': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de4e0>,\n",
       " 'resolution': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de518>,\n",
       " 'pursue': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de588>,\n",
       " 'refrain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de5f8>,\n",
       " 'unreasonable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de630>,\n",
       " 'anger': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de6a0>,\n",
       " 'missing': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de710>,\n",
       " 'former': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de780>,\n",
       " 'income': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de7f0>,\n",
       " 'still': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de860>,\n",
       " 'comparison': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de898>,\n",
       " 'Enscombe': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de8d0>,\n",
       " 'cease': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de940>,\n",
       " 'once': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de9b0>,\n",
       " 'considered': <gensim.models.keyedvectors.Vocab at 0x7fbfce9de9e8>,\n",
       " 'especially': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dea20>,\n",
       " 'Churchills': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dea58>,\n",
       " 'amazing': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deac8>,\n",
       " 'worst': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deb38>,\n",
       " 'bargain': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deba8>,\n",
       " 'poorer': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dec18>,\n",
       " 'child': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dec88>,\n",
       " 'From': <gensim.models.keyedvectors.Vocab at 0x7fbfce9decf8>,\n",
       " 'expense': <gensim.models.keyedvectors.Vocab at 0x7fbfce9ded68>,\n",
       " 'relieved': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deda0>,\n",
       " 'boy': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee10>,\n",
       " 'additional': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee48>,\n",
       " 'softening': <gensim.models.keyedvectors.Vocab at 0x7fbfce9dee80>,\n",
       " 'lingering': <gensim.models.keyedvectors.Vocab at 0x7fbfce9deeb8>,\n",
       " 'illness': <gensim.models.keyedvectors.Vocab at 0x7fbfce9def28>,\n",
       " 'reconciliation': <gensim.models.keyedvectors.Vocab at 0x7fbfce9def60>,\n",
       " 'kindred': <gensim.models.keyedvectors.Vocab at 0x7fbfce9defd0>,\n",
       " 'offered': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7080>,\n",
       " 'charge': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b70f0>,\n",
       " 'Frank': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7160>,\n",
       " 'decease': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b71d0>,\n",
       " 'scruples': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7208>,\n",
       " 'reluctance': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7240>,\n",
       " 'supposed': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7278>,\n",
       " 'overcome': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b72b0>,\n",
       " 'considerations': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b72e8>,\n",
       " 'wealth': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7358>,\n",
       " 'seek': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b73c8>,\n",
       " 'improve': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7438>,\n",
       " 'complete': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7470>,\n",
       " 'became': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b74e0>,\n",
       " 'desirable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7518>,\n",
       " 'quitted': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7588>,\n",
       " 'trade': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b75f8>,\n",
       " 'established': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7630>,\n",
       " 'favourable': <gensim.models.keyedvectors.Vocab at 0x7fbfce9b7668>,\n",
       " ...}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17011"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.wv.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = model[model.wv.vocab]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, n_iter=1000) # 200 is minimum iter; default is 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_2d = tsne.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.93104606, -3.94591487],\n",
       "       [ 3.72858522, -3.21834929],\n",
       "       [ 3.11308347, -3.13188365],\n",
       "       [ 5.1765412 ,  2.32805661],\n",
       "       [ 3.92752917, -3.95470727]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2d[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create DataFrame for storing results and plotting\n",
    "coords_df = pd.DataFrame(X_2d, columns=['x','y'])\n",
    "coords_df['token'] = model.wv.vocab.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>token</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.931046</td>\n",
       "      <td>-3.945915</td>\n",
       "      <td>[</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.728585</td>\n",
       "      <td>-3.218349</td>\n",
       "      <td>Emma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.113083</td>\n",
       "      <td>-3.131884</td>\n",
       "      <td>by</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.176541</td>\n",
       "      <td>2.328057</td>\n",
       "      <td>Jane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.927529</td>\n",
       "      <td>-3.954707</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y token\n",
       "0  3.931046 -3.945915     [\n",
       "1  3.728585 -3.218349  Emma\n",
       "2  3.113083 -3.131884    by\n",
       "3  5.176541  2.328057  Jane\n",
       "4  3.927529 -3.954707     ]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# coords_df.to_csv('raw_gutenberg_tsne.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize 2D representation of word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coords_df = pd.read_csv('raw_gutenberg_tsne.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CJGYmA4hJglVwhR1veSqE9KECUfBheUrCeraCvFK3XIrysoa9koqPNnPQDCBXqeP1VAJA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Dah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\nxiC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1pPe5+dvcmf4KGTkSN1cyUk1W2iTCiuIoa93VDdw9aEAfjnG5mAr0MhK3lHRJLW7i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o1ORpHw9Q3nZu/f/clt3NtrIJeMOwwzOR6FKsccQU1+795aycq2uXvSQ6c/xnxG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\n4e52Dbs1DWulFP5oveS6xktpg1xXzlnqKfQ761WOoPUXgW5/omSRS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TvCMV5/xOXEY1wJ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\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Z1C4sYRXHsIimKBLHLmJEn2VlK6Lj50WCyzkV96v52XPCHMOSiZsl4JO9YaDAA1tqm9ej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\nhjGV7RMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfcf94c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = coords_df.plot.scatter('x', 'y', figsize=(12,12), marker='.', s=10, alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"742e276f-4d1b-49ed-aeb1-baf34db8c776\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      var el = document.getElementById(\"742e276f-4d1b-49ed-aeb1-baf34db8c776\");\n",
       "      el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete window._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"742e276f-4d1b-49ed-aeb1-baf34db8c776\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '742e276f-4d1b-49ed-aeb1-baf34db8c776' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"742e276f-4d1b-49ed-aeb1-baf34db8c776\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"742e276f-4d1b-49ed-aeb1-baf34db8c776\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook() # output bokeh plots inline in notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset_df = coords_df.sample(n=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = figure(plot_width=800, plot_height=800)\n",
    "_ = p.text(x=subset_df.x, y=subset_df.y, text=subset_df.token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"78e6affd-d8ae-47cd-ba84-e889eeead1d1\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"78e6affd-d8ae-47cd-ba84-e889eeead1d1\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
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       "        setTimeout(display_loaded, 100)\n",
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       "  \n",
       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"78e6affd-d8ae-47cd-ba84-e889eeead1d1\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '78e6affd-d8ae-47cd-ba84-e889eeead1d1' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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\",\"dtype\":\"float64\",\"shape\":[5000]},\"y\":{\"__ndarray__\":\"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       "            var render_items = [{\"docid\":\"47ab1de4-7e55-41d2-9aa0-7e77f6f2b7f7\",\"elementid\":\"78e6affd-d8ae-47cd-ba84-e889eeead1d1\",\"modelid\":\"155b46ee-15f4-40eb-b191-117fb43afacf\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
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       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
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       "  \n",
       "    function run_inline_js() {\n",
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       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"78e6affd-d8ae-47cd-ba84-e889eeead1d1\")).parents('.cell').data().cell;\n",
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       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
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